Research on residual current monitoring system of substation considering inherent accuracy error of transformer
A stable station power supply system is the foundation for reliable operation of production equipment in substations.Any problem with the station power system will directly or indirectly affect the safety of the substation.Currently,the insulation monitoring of substation station power is mainly achieved through real-time monitoring of residual currents in the substation power system.However,the residual current monitoring method is mainly aimed at scenarios involving the fusion measurement of multiple current transformers.Existing measurement systems do not consider the influence of transformer deviations during the fusion process,resulting in frequent false alarm condi-tions.In view of this,this paper proposes a substation station power residual current monitoring system that takes into account the fusion deviation of current transformers.This system synchronously acquires and real-time synthesi-zes residual current data from multiple-channel current transformers,and uses the Autoregressive Integrated Moving Average Model(ARIMA model)to perform time-series modeling and anomaly detection on residual currents.Then,a wavelet threshold denoising algorithm is used to denoise the repaired residual current sequence,achieving efficient and accurate monitoring of residual currents.To verify the effectiveness of the proposed system,field tests were conducted at substations.The results show that the system can successfully identify and repair abnormal resid-ual current data with significant denoising effects.The maximum absolute error of the repaired data is only 5.9 mA.Compared with the measurement data of 0.5S-class transformers,the average absolute percentage error and root mean square error of the denoised data are reduced by 0.024 and 1.222,respectively.
residual current monitoring systemtransformer fusion deviationARIMA modelwavelet threshold denoising
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